Fouling fault detection and diagnosis in district heating substations: Validation of a hybrid CNN-based PCA model with uncertainty quantification on virtual replica synthesis and real data

This research presents an advanced fouling fault detection (FFD) model tailored to district heating substations, integrating convolutional neural networks, principal component analysis, and uncertainty quantification (UQ). Through careful hyperparameter tuning, the model attained optimal configurations, showcasing accuracy metrics ranging from 96.58 % to 98.19 %. Key findings include the influence of input vectors, particularly the incorporation of overall heat transfer coefficient (UA), which contributed to heightened accuracy and precision. Visualizing predictive uncertainty emphasized the model's adaptability to dynamic conditions, crucial in scenarios involving progressive fault evolution. Generalizability analysis across diverse substations affirmed the model's adaptability, surpassing existing algorithms. Comparative evaluation against recent models underscored the proposed model's superiority, with an accuracy range exceeding 96.58 %. This research introduces UQ as a unique advantage, offering a state-of-the-art solution for FFD in dynamic systems. The model's validation on real data further solidifies its efficacy.